Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
A Novel in Silico Platform for a Fully Automatic Personalized Brain Tumor Growth Publisher Pubmed



Hajishamsaei M1 ; Pishevar A1 ; Bavi O2 ; Soltani M3, 4, 5, 6, 7
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran
  2. 2. Department of Mechanical and Aerospace Engineering, Shiraz University of Technology, Shiraz, Iran
  3. 3. Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
  4. 4. Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran
  5. 5. Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
  6. 6. Department of Electrical and Computer Engineering, University of Waterloo, ON, Canada
  7. 7. Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Ontario, Canada

Source: Magnetic Resonance Imaging Published:2020


Abstract

Glioblastoma Multiforme is the most common and most aggressive type of brain tumors grade four astrocytoma. Although accurate prediction of Glioblastoma borders and shape is absolutely essential for neurosurgeons, there are not many in silico platforms that can make such predictions. In the current study, an automatic patient-specific simulation of Glioblastoma growth is described. A finite element approach is used to analyze the magnetic resonance images from patients in the early stages of their tumors. For segmentation of the tumor, support vector machine method, which is an automatic segmentation algorithm, is used. Using in situ and in vivo data, the main parameters of tumor prediction and growth are estimated with high precision in proliferation-invasion partial differential equation, using genetic algorithm optimization method. The results show that for a C57BL mouse, the differences between the surface and perimeter of in vivo test and simulation prediction data, as objective function, are 3.7% and 17.4%, respectively. © 2020 Elsevier Inc.
Related Docs
1. Personalized Image-Based Tumor Growth Prediction in a Convection–Diffusion–Reaction Model, Acta Neurologica Belgica (2020)
2. Image Based Modeling of Tumor Growth, Australasian Physical and Engineering Sciences in Medicine (2016)